diff --git a/GPy/examples/classification.py b/GPy/examples/classification.py
index 989ed08a..fb14139d 100644
--- a/GPy/examples/classification.py
+++ b/GPy/examples/classification.py
@@ -76,11 +76,10 @@ def toy_linear_1d_classification(model_type='Full', inducing=4, seed=default_see
 
     # create simple GP model
     if model_type=='Full':
-        m = GPy.models.simple_GP_EP(data['X'],likelihood)
+        m = GPy.models.GP_EP(data['X'],likelihood)
     else:
         # create sparse GP EP model
         m = GPy.models.sparse_GP_EP(data['X'],likelihood=likelihood,inducing=inducing,ep_proxy=model_type)
-            
 
     m.constrain_positive('var')
     m.constrain_positive('len')
diff --git a/GPy/examples/ep_fix.py b/GPy/examples/ep_fix.py
new file mode 100644
index 00000000..e4999f30
--- /dev/null
+++ b/GPy/examples/ep_fix.py
@@ -0,0 +1,39 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+"""
+Simple Gaussian Processes classification
+"""
+import pylab as pb
+import numpy as np
+import GPy
+pb.ion()
+
+default_seed=10000
+
+model_type='Full'
+inducing=4
+seed=default_seed
+"""Simple 1D classification example.
+:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
+:param seed : seed value for data generation (default is 4).
+:type seed: int
+:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
+:type inducing: int
+"""
+data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
+likelihood = GPy.inference.likelihoods.probit(data['Y'][:, 0:1])
+
+m = GPy.models.GP_EP2(data['X'],likelihood)
+
+#m.constrain_positive('var')
+#m.constrain_positive('len')
+#m.tie_param('lengthscale')
+m.approximate_likelihood()
+# Optimize and plot
+#m.optimize()
+#m.em(plot_all=False) # EM algorithm
+m.plot()
+
+print(m)
diff --git a/GPy/inference/EP.py b/GPy/inference/EP.py
new file mode 100644
index 00000000..fa691961
--- /dev/null
+++ b/GPy/inference/EP.py
@@ -0,0 +1,314 @@
+import numpy as np
+import random
+import pylab as pb #TODO erase me
+from scipy import stats, linalg
+from .likelihoods import likelihood
+from ..core import model
+from ..util.linalg import pdinv,mdot,jitchol
+from ..util.plot import gpplot
+from .. import kern
+
+class EP:
+    def __init__(self,covariance,likelihood,Kmn=None,Knn_diag=None,epsilon=1e-3,powerep=[1.,1.]):
+        """
+        Expectation Propagation
+
+        Arguments
+        ---------
+        X : input observations
+        likelihood : Output's likelihood (likelihood class)
+        kernel :  a GPy kernel (kern class)
+        inducing : Either an array specifying the inducing points location or a sacalar defining their number. None value for using a non-sparse model is used.
+        powerep : Power-EP parameters (eta,delta) - 2x1 numpy array (floats)
+        epsilon : Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
+        """
+        self.likelihood = likelihood
+        assert covariance.shape[0] == covariance.shape[1]
+        if Kmn is not None:
+            self.Kmm = covariance
+            self.Kmn = Kmn
+            self.M = self.Kmn.shape[0]
+            self.N = self.Kmn.shape[1]
+            assert self.M < self.N, 'The number of inducing inputs must be smaller than the number of observations'
+        else:
+            self.K = covariance
+            self.N = self.K.shape[0]
+        if Knn_diag is not None:
+            self.Knn_diag = Knn_diag
+            assert len(Knn_diag) == self.N, 'Knn_diagonal has size different from N'
+
+        self.epsilon = epsilon
+        self.eta, self.delta = powerep
+        self.jitter = 1e-12
+
+        """
+        Initial values - Likelihood approximation parameters:
+        p(y|f) = t(f|tau_tilde,v_tilde)
+        """
+        self.tau_tilde = np.zeros(self.N)
+        self.v_tilde = np.zeros(self.N)
+
+    def restart_EP(self):
+        """
+        Set the EP approximation to initial state
+        """
+        self.tau_tilde = np.zeros(self.N)
+        self.v_tilde = np.zeros(self.N)
+        self.mu = np.zeros(self.N)
+
+class Full(EP):
+    def fit_EP(self):
+        """
+        The expectation-propagation algorithm.
+        For nomenclature see Rasmussen & Williams 2006 (pag. 52-60)
+        """
+        #Prior distribution parameters: p(f|X) = N(f|0,K)
+        #self.K = self.kernel.K(self.X,self.X)
+
+        #Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma)
+        self.mu=np.zeros(self.N)
+        self.Sigma=self.K.copy()
+
+        """
+        Initial values - Cavity distribution parameters:
+        q_(f|mu_,sigma2_) = Product{q_i(f|mu_i,sigma2_i)}
+        sigma_ = 1./tau_
+        mu_ = v_/tau_
+        """
+        self.tau_ = np.empty(self.N,dtype=float)
+        self.v_ = np.empty(self.N,dtype=float)
+
+        #Initial values - Marginal moments
+        z = np.empty(self.N,dtype=float)
+        self.Z_hat = np.empty(self.N,dtype=float)
+        phi = np.empty(self.N,dtype=float)
+        mu_hat = np.empty(self.N,dtype=float)
+        sigma2_hat = np.empty(self.N,dtype=float)
+        self.mu_hat = mu_hat #TODO erase me
+        self.sigma2_hat = sigma2_hat #TODO erase me
+
+        #Approximation
+        epsilon_np1 = self.epsilon + 1.
+        epsilon_np2 = self.epsilon + 1.
+       	self.iterations = 0
+        self.np1 = [self.tau_tilde.copy()]
+        self.np2 = [self.v_tilde.copy()]
+        while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
+            update_order = np.arange(self.N)
+            #random.shuffle(update_order) #TODO uncomment
+            for i in update_order:
+                #Cavity distribution parameters
+                self.tau_[i] = 1./self.Sigma[i,i] - self.eta*self.tau_tilde[i]
+                self.v_[i] = self.mu[i]/self.Sigma[i,i] - self.eta*self.v_tilde[i]
+                #Marginal moments
+                self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood.moments_match(i,self.tau_[i],self.v_[i])
+                self.mu_hat[i] = mu_hat[i] #TODO erase me
+                self.sigma2_hat[i] = sigma2_hat[i] #TODO erase me
+                #if i == 3:
+                #    a = b
+                #Site parameters update
+                Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./self.Sigma[i,i])
+                Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - self.mu[i]/self.Sigma[i,i])
+                print Delta_tau
+                self.tau_tilde[i] = self.tau_tilde[i] + Delta_tau
+                self.v_tilde[i] = self.v_tilde[i] + Delta_v
+                #Posterior distribution parameters update
+                si=self.Sigma[:,i].reshape(self.N,1)
+                self.Sigma = self.Sigma - Delta_tau/(1.+ Delta_tau*self.Sigma[i,i])*np.dot(si,si.T)
+                self.mu = np.dot(self.Sigma,self.v_tilde)
+                self.iterations += 1
+            #Sigma recomptutation with Cholesky decompositon
+            Sroot_tilde_K = np.sqrt(self.tau_tilde)[:,None]*(self.K)
+            B = np.eye(self.N) + np.sqrt(self.tau_tilde)[None,:]*Sroot_tilde_K
+            L = jitchol(B)
+            V,info = linalg.flapack.dtrtrs(L,Sroot_tilde_K,lower=1)
+            self.Sigma = self.K - np.dot(V.T,V)
+            self.mu = np.dot(self.Sigma,self.v_tilde)
+            epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N
+            epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N
+            self.np1.append(self.tau_tilde.copy())
+            self.np2.append(self.v_tilde.copy())
+
+class DTC(EP):
+    def fit_EP(self):
+        """
+        The expectation-propagation algorithm with sparse pseudo-input.
+        For nomenclature see ... 2013.
+        """
+
+        """
+        Prior approximation parameters:
+        q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)
+        Sigma0 = Qnn = Knm*Kmmi*Kmn
+        """
+        self.Kmmi, self.Kmm_hld = pdinv(self.Kmm)
+        self.KmnKnm = np.dot(self.Kmn, self.Kmn.T)
+        self.KmmiKmn = np.dot(self.Kmmi,self.Kmn)
+        self.Qnn_diag = np.sum(self.Kmn*self.KmmiKmn,-2)
+        self.LLT0 = self.Kmm.copy()
+
+        """
+        Posterior approximation: q(f|y) = N(f| mu, Sigma)
+        Sigma = Diag + P*R.T*R*P.T + K
+        mu = w + P*gamma
+        """
+        self.mu = np.zeros(self.N)
+        self.LLT = self.Kmm.copy()
+        self.Sigma_diag = self.Qnn_diag.copy()
+
+        """
+        Initial values - Cavity distribution parameters:
+        q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}
+        sigma_ = 1./tau_
+        mu_ = v_/tau_
+        """
+        self.tau_ = np.empty(self.N,dtype=float)
+        self.v_ = np.empty(self.N,dtype=float)
+
+        #Initial values - Marginal moments
+        z = np.empty(self.N,dtype=float)
+        self.Z_hat = np.empty(self.N,dtype=float)
+        phi = np.empty(self.N,dtype=float)
+        mu_hat = np.empty(self.N,dtype=float)
+        sigma2_hat = np.empty(self.N,dtype=float)
+
+        #Approximation
+        epsilon_np1 = 1
+        epsilon_np2 = 1
+       	self.iterations = 0
+        self.np1 = [self.tau_tilde.copy()]
+        self.np2 = [self.v_tilde.copy()]
+        while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
+            update_order = np.arange(self.N)
+            random.shuffle(update_order)
+            for i in update_order:
+                #Cavity distribution parameters
+                self.tau_[i] = 1./self.Sigma_diag[i] - self.eta*self.tau_tilde[i]
+                self.v_[i] = self.mu[i]/self.Sigma_diag[i] - self.eta*self.v_tilde[i]
+                #Marginal moments
+                self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood.moments_match(i,self.tau_[i],self.v_[i])
+                #Site parameters update
+                Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./self.Sigma_diag[i])
+                Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - self.mu[i]/self.Sigma_diag[i])
+                self.tau_tilde[i] = self.tau_tilde[i] + Delta_tau
+                self.v_tilde[i] = self.v_tilde[i] + Delta_v
+                #Posterior distribution parameters update
+                self.LLT = self.LLT + np.outer(self.Kmn[:,i],self.Kmn[:,i])*Delta_tau
+                L = jitchol(self.LLT)
+                V,info = linalg.flapack.dtrtrs(L,self.Kmn,lower=1)
+                self.Sigma_diag = np.sum(V*V,-2)
+                si = np.sum(V.T*V[:,i],-1)
+                self.mu = self.mu + (Delta_v-Delta_tau*self.mu[i])*si
+                self.iterations += 1
+            #Sigma recomputation with Cholesky decompositon
+            self.LLT0 = self.LLT0 + np.dot(self.Kmn*self.tau_tilde[None,:],self.Kmn.T)
+            self.L = jitchol(self.LLT)
+            V,info = linalg.flapack.dtrtrs(L,self.Kmn,lower=1)
+            V2,info = linalg.flapack.dtrtrs(L.T,V,lower=0)
+            self.Sigma_diag = np.sum(V*V,-2)
+            Knmv_tilde = np.dot(self.Kmn,self.v_tilde)
+            self.mu = np.dot(V2.T,Knmv_tilde)
+            epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N
+            epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N
+            self.np1.append(self.tau_tilde.copy())
+            self.np2.append(self.v_tilde.copy())
+
+class FITC(EP):
+    def fit_EP(self):
+        """
+        The expectation-propagation algorithm with sparse pseudo-input.
+        For nomenclature see Naish-Guzman and Holden, 2008.
+        """
+
+        """
+        Prior approximation parameters:
+        q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)
+        Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn
+        """
+        self.Kmmi, self.Kmm_hld = pdinv(self.Kmm)
+        self.P0 = self.Kmn.T
+        self.KmnKnm = np.dot(self.P0.T, self.P0)
+        self.KmmiKmn = np.dot(self.Kmmi,self.P0.T)
+        self.Qnn_diag = np.sum(self.P0.T*self.KmmiKmn,-2)
+        self.Diag0 = self.Knn_diag - self.Qnn_diag
+        self.R0 = jitchol(self.Kmmi).T
+
+        """
+        Posterior approximation: q(f|y) = N(f| mu, Sigma)
+        Sigma = Diag + P*R.T*R*P.T + K
+        mu = w + P*gamma
+        """
+        self.w = np.zeros(self.N)
+        self.gamma = np.zeros(self.M)
+        self.mu = np.zeros(self.N)
+        self.P = self.P0.copy()
+        self.R = self.R0.copy()
+        self.Diag = self.Diag0.copy()
+        self.Sigma_diag = self.Knn_diag
+
+        """
+        Initial values - Cavity distribution parameters:
+        q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}
+        sigma_ = 1./tau_
+        mu_ = v_/tau_
+        """
+        self.tau_ = np.empty(self.N,dtype=float)
+        self.v_ = np.empty(self.N,dtype=float)
+
+        #Initial values - Marginal moments
+        z = np.empty(self.N,dtype=float)
+        self.Z_hat = np.empty(self.N,dtype=float)
+        phi = np.empty(self.N,dtype=float)
+        mu_hat = np.empty(self.N,dtype=float)
+        sigma2_hat = np.empty(self.N,dtype=float)
+
+        #Approximation
+        epsilon_np1 = 1
+        epsilon_np2 = 1
+       	self.iterations = 0
+        self.np1 = [self.tau_tilde.copy()]
+        self.np2 = [self.v_tilde.copy()]
+        while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
+            update_order = np.arange(self.N)
+            random.shuffle(update_order)
+            for i in update_order:
+                #Cavity distribution parameters
+                self.tau_[i] = 1./self.Sigma_diag[i] - self.eta*self.tau_tilde[i]
+                self.v_[i] = self.mu[i]/self.Sigma_diag[i] - self.eta*self.v_tilde[i]
+                #Marginal moments
+                self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood.moments_match(i,self.tau_[i],self.v_[i])
+                #Site parameters update
+                Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./self.Sigma_diag[i])
+                Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - self.mu[i]/self.Sigma_diag[i])
+                self.tau_tilde[i] = self.tau_tilde[i] + Delta_tau
+                self.v_tilde[i] = self.v_tilde[i] + Delta_v
+                #Posterior distribution parameters update
+                dtd1 = Delta_tau*self.Diag[i] + 1.
+                dii = self.Diag[i]
+                self.Diag[i] = dii - (Delta_tau * dii**2.)/dtd1
+                pi_ = self.P[i,:].reshape(1,self.M)
+                self.P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_
+                Rp_i = np.dot(self.R,pi_.T)
+                RTR = np.dot(self.R.T,np.dot(np.eye(self.M) - Delta_tau/(1.+Delta_tau*self.Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),self.R))
+                self.R = jitchol(RTR).T
+                self.w[i] = self.w[i] + (Delta_v - Delta_tau*self.w[i])*dii/dtd1
+                self.gamma = self.gamma + (Delta_v - Delta_tau*self.mu[i])*np.dot(RTR,self.P[i,:].T)
+                self.RPT = np.dot(self.R,self.P.T)
+                self.Sigma_diag = self.Diag + np.sum(self.RPT.T*self.RPT.T,-1)
+                self.mu = self.w + np.dot(self.P,self.gamma)
+                self.iterations += 1
+            #Sigma recomptutation with Cholesky decompositon
+            self.Diag = self.Diag0/(1.+ self.Diag0 * self.tau_tilde)
+            self.P = (self.Diag / self.Diag0)[:,None] * self.P0
+            self.RPT0 = np.dot(self.R0,self.P0.T)
+            L = jitchol(np.eye(self.M) + np.dot(self.RPT0,(1./self.Diag0 - self.Diag/(self.Diag0**2))[:,None]*self.RPT0.T))
+            self.R,info = linalg.flapack.dtrtrs(L,self.R0,lower=1)
+            self.RPT = np.dot(self.R,self.P.T)
+            self.Sigma_diag = self.Diag + np.sum(self.RPT.T*self.RPT.T,-1)
+            self.w = self.Diag * self.v_tilde
+            self.gamma = np.dot(self.R.T, np.dot(self.RPT,self.v_tilde))
+            self.mu = self.w + np.dot(self.P,self.gamma)
+            epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N
+            epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N
+            self.np1.append(self.tau_tilde.copy())
+            self.np2.append(self.v_tilde.copy())
diff --git a/GPy/inference/Expectation_Propagation.py b/GPy/inference/Expectation_Propagation.py
index 05453f1d..520fc607 100644
--- a/GPy/inference/Expectation_Propagation.py
+++ b/GPy/inference/Expectation_Propagation.py
@@ -116,7 +116,7 @@ class Full(EP_base):
             self.np1.append(self.tau_tilde.copy())
             self.np2.append(self.v_tilde.copy())
             if messages:
-                print "EP iteration %i, epsiolon %d"%(self.iterations,epsilon_np1)
+                print "EP iteration %i, epsilon %d"%(self.iterations,epsilon_np1)
 
 class FITC(EP_base):
     """
diff --git a/GPy/inference/likelihoods.py b/GPy/inference/likelihoods.py
index 5f0eb7ff..ff4770f6 100644
--- a/GPy/inference/likelihoods.py
+++ b/GPy/inference/likelihoods.py
@@ -32,7 +32,7 @@ class likelihood:
         """
         assert X_new.shape[1] == 1, 'Number of dimensions must be 1'
         gpplot(X_new,Mean_new,Var_new)
-        pb.errorbar(X_u,Mean_u,2*np.sqrt(Var_u),fmt='r+')
+        pb.errorbar(X_u.flatten(),Mean_u.flatten(),2*np.sqrt(Var_u.flatten()),fmt='r+')
         pb.plot(X_u,Mean_u,'ro')
 
     def plot2D(self,X,X_new,F_new,U=None):
diff --git a/GPy/models/GP_EP.py b/GPy/models/GP_EP.py
index 51d69d0a..302ff366 100644
--- a/GPy/models/GP_EP.py
+++ b/GPy/models/GP_EP.py
@@ -57,7 +57,7 @@ class GP_EP(model):
     def posterior_param(self):
         self.K = self.kernel.K(self.X)
         self.Sroot_tilde_K =  np.sqrt(self.ep_approx.tau_tilde)[:,None]*self.K
-        B = np.eye(self.N) + np.sqrt(self.ep_approx.tau_tilde)[None,:]*self.Sroot_tilde_K
+        B = np.eye(self.N) + np.sqrt(self.ep_approx.tau_tilde)*self.Sroot_tilde_K
         #self.L = np.linalg.cholesky(B)
         self.L = jitchol(B)
         V,info = linalg.flapack.dtrtrs(self.L,self.Sroot_tilde_K,lower=1)
diff --git a/GPy/models/GP_EP2.py b/GPy/models/GP_EP2.py
new file mode 100644
index 00000000..c68e7b70
--- /dev/null
+++ b/GPy/models/GP_EP2.py
@@ -0,0 +1,280 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import numpy as np
+import pylab as pb
+from scipy import stats, linalg
+from .. import kern
+from ..inference.EP import Full
+from ..inference.likelihoods import likelihood,probit,poisson,gaussian
+from ..core import model
+from ..util.linalg import pdinv,mdot #,jitchol
+from ..util.plot import gpplot, Tango
+
+class GP_EP2(model):
+    def __init__(self,X,likelihood,kernel=None,normalize_X=False,Xslices=None,epsilon_ep=1e-3,epsion_em=.1,powerep=[1.,1.]):
+        """
+        Simple Gaussian Process with Non-Gaussian likelihood
+
+        Arguments
+        ---------
+        :param X: input observations (NxD numpy.darray)
+        :param likelihood: a GPy likelihood (likelihood class)
+        :param kernel: a GPy kernel, defaults to rbf+white
+        :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
+        :type normalize_X: False|True
+        :param epsilon_ep: convergence criterion for the Expectation Propagation algorithm, defaults to 1e-3
+        :param powerep: power-EP parameters [$\eta$,$\delta$], defaults to [1.,1.] (list)
+        :param Xslices: how the X,Y data co-vary in the kernel (i.e. which "outputs" they correspond to). See (link:slicing)
+        :rtype: model object.
+        """
+        #.. Note:: Multiple independent outputs are allowed using columns of Y #TODO add this note?
+        if kernel is None:
+            kernel = kern.rbf(X.shape[1]) + kern.bias(X.shape[1]) + kern.white(X.shape[1])
+
+        # parse arguments
+        self.Xslices = Xslices
+        assert isinstance(kernel, kern.kern)
+        self.likelihood = likelihood
+        #self.Y = self.likelihood.Y #we might not need this
+        self.kern = kernel
+        self.X = X
+        assert len(self.X.shape)==2
+        #assert len(self.Y.shape)==2
+        #assert self.X.shape[0] == self.Y.shape[0]
+        #self.N, self.D = self.Y.shape
+        self.D = 1
+        self.N, self.Q = self.X.shape
+
+        #here's some simple normalisation
+        if normalize_X:
+            self._Xmean = X.mean(0)[None,:]
+            self._Xstd = X.std(0)[None,:]
+            self.X = (X.copy() - self._Xmean) / self._Xstd
+            if hasattr(self,'Z'):
+                self.Z = (self.Z - self._Xmean) / self._Xstd
+        else:
+            self._Xmean = np.zeros((1,self.X.shape[1]))
+            self._Xstd = np.ones((1,self.X.shape[1]))
+
+        #THIS PART IS NOT NEEDED
+        """
+        if normalize_Y:
+            self._Ymean = Y.mean(0)[None,:]
+            self._Ystd = Y.std(0)[None,:]
+            self.Y = (Y.copy()- self._Ymean) / self._Ystd
+        else:
+            self._Ymean = np.zeros((1,self.Y.shape[1]))
+            self._Ystd = np.ones((1,self.Y.shape[1]))
+
+        if self.D > self.N:
+            # then it's more efficient to store YYT
+            self.YYT = np.dot(self.Y, self.Y.T)
+        else:
+            self.YYT = None
+        """
+        self.eta,self.delta = powerep
+        self.epsilon_ep = epsilon_ep
+        self.tau_tilde = np.zeros([self.N,self.D])
+        self.v_tilde = np.zeros([self.N,self.D])
+        model.__init__(self)
+
+    def _set_params(self,p):
+        self.kern._set_params_transformed(p)
+        self.K = self.kern.K(self.X,slices1=self.Xslices)
+        self.posterior_params()
+
+    def _get_params(self):
+        return self.kern._get_params_transformed()
+
+    def _get_param_names(self):
+        return self.kern._get_param_names_transformed()
+
+    def approximate_likelihood(self):
+        self.ep_approx = Full(self.K,self.likelihood,epsilon=self.epsilon_ep,powerep=[self.eta,self.delta])
+        self.ep_approx.fit_EP()
+        self.tau_tilde = self.ep_approx.tau_tilde[:,None]
+        self.v_tilde = self.ep_approx.tau_tilde[:,None]
+        self.posterior_params()
+        self.Y = self.v_tilde/self.tau_tilde
+        self._Ymean = np.zeros((1,self.Y.shape[1]))
+        self._Ystd = np.ones((1,self.Y.shape[1]))
+        #self.YYT = np.dot(self.Y, self.Y.T)
+
+    def posterior_params(self):
+        self.Sroot_tilde_K =  np.sqrt(self.tau_tilde.flatten())[:,None]*self.K
+        B = np.eye(self.N) + np.sqrt(self.tau_tilde.flatten())[None,:]*self.Sroot_tilde_K
+        self.Bi,self.L,self.Li,B_logdet = pdinv(B)
+        V = np.dot(self.Li,self.Sroot_tilde_K)
+        #V,info = linalg.flapack.dtrtrs(self.L,self.Sroot_tilde_K,lower=1)
+        self.Sigma = self.K - np.dot(V.T,V)
+        self.mu = np.dot(self.Sigma,self.v_tilde.flatten())
+
+
+    #def _model_fit_term(self):
+    #    """
+    #    Computes the model fit using YYT if it's available
+    #    """
+    #    if self.YYT is None:
+    #        return -0.5*np.sum(np.square(np.dot(self.Li,self.Y)))
+    #    else:
+    #        return -0.5*np.sum(np.multiply(self.Ki, self.YYT))
+
+    def log_likelihood(self):
+        mu_ = self.ep_approx.v_/self.ep_approx.tau_
+        L1 =.5*sum(np.log(1+self.ep_approx.tau_tilde*1./self.ep_approx.tau_))-sum(np.log(np.diag(self.L)))
+        L2A =.5*np.sum((self.Sigma-np.diag(1./(self.ep_approx.tau_+self.ep_approx.tau_tilde))) * np.dot(self.ep_approx.v_tilde[:,None],self.ep_approx.v_tilde[None,:]))
+        L2B = .5*np.dot(mu_*(self.ep_approx.tau_/(self.ep_approx.tau_tilde+self.ep_approx.tau_)),self.ep_approx.tau_tilde*mu_ - 2*self.ep_approx.v_tilde)
+        L3 = sum(np.log(self.ep_approx.Z_hat))
+        return L1 + L2A + L2B + L3
+
+    def dL_dK(self): #FIXME
+        if self.YYT is None:
+            alpha = np.dot(self.Ki,self.Y)
+            dL_dK = 0.5*(np.dot(alpha,alpha.T)-self.D*self.Ki)
+        else:
+            dL_dK = 0.5*(mdot(self.Ki, self.YYT, self.Ki) - self.D*self.Ki)
+
+        return dL_dK
+
+    def _log_likelihood_gradients(self): #FIXME
+        return self.kern.dK_dtheta(partial=self.dL_dK(),X=self.X)
+
+    def predict(self,Xnew, slices=None, full_cov=False):
+        """
+
+        Predict the function(s) at the new point(s) Xnew.
+
+        Arguments
+        ---------
+        :param Xnew: The points at which to make a prediction
+        :type Xnew: np.ndarray, Nnew x self.Q
+        :param slices:  specifies which outputs kernel(s) the Xnew correspond to (see below)
+        :type slices: (None, list of slice objects, list of ints)
+        :param full_cov: whether to return the folll covariance matrix, or just the diagonal
+        :type full_cov: bool
+        :rtype: posterior mean,  a Numpy array, Nnew x self.D
+        :rtype: posterior variance, a Numpy array, Nnew x Nnew x (self.D)
+
+        .. Note:: "slices" specifies how the the points X_new co-vary wich the training points.
+
+             - If None, the new points covary throigh every kernel part (default)
+             - If a list of slices, the i^th slice specifies which data are affected by the i^th kernel part
+             - If a list of booleans, specifying which kernel parts are active
+
+           If full_cov and self.D > 1, the return shape of var is Nnew x Nnew x self.D. If self.D == 1, the return shape is Nnew x Nnew.
+           This is to allow for different normalisations of the output dimensions.
+
+
+        """
+
+        #normalise X values
+        Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
+        mu, var, phi = self._raw_predict(Xnew, slices, full_cov)
+
+        #un-normalise
+        mu = mu*self._Ystd + self._Ymean
+        if full_cov:
+            if self.D==1:
+                var *= np.square(self._Ystd)
+            else:
+                var = var[:,:,None] * np.square(self._Ystd)
+        else:
+            if self.D==1:
+                var *= np.square(np.squeeze(self._Ystd))
+            else:
+                var = var[:,None] * np.square(self._Ystd)
+
+        return mu,var,phi
+
+    def _raw_predict(self,_Xnew,slices, full_cov=False):
+        """Internal helper function for making predictions, does not account for normalisation"""
+        """
+        Kx = self.kern.K(self.X,_Xnew, slices1=self.Xslices,slices2=slices)
+        mu = np.dot(np.dot(Kx.T,self.Ki),self.Y)
+        KiKx = np.dot(self.Ki,Kx)
+        if full_cov:
+            Kxx = self.kern.K(_Xnew, slices1=slices,slices2=slices)
+            var = Kxx - np.dot(KiKx.T,Kx)
+        else:
+            Kxx = self.kern.Kdiag(_Xnew, slices=slices)
+            var = Kxx - np.sum(np.multiply(KiKx,Kx),0)
+        return mu, var
+        """
+        K_x = self.kern.K(self.X,_Xnew)
+        Kxx = self.kern.K(_Xnew)
+        #aux1,info = linalg.flapack.dtrtrs(self.L,np.dot(self.Sroot_tilde_K,self.ep_approx.v_tilde),lower=1)
+        #aux2,info = linalg.flapack.dtrtrs(self.L.T, aux1,lower=0)
+        #aux2 = mdot(self.Li.T,self.Li,self.Sroot_tilde_K,self.ep_approx.v_tilde)
+        aux2 = mdot(self.Bi,self.Sroot_tilde_K,self.ep_approx.v_tilde)
+        zeta = np.sqrt(self.ep_approx.tau_tilde)*aux2
+        f = np.dot(K_x.T,self.ep_approx.v_tilde-zeta)
+       	#v,info = linalg.flapack.dtrtrs(self.L,np.sqrt(self.ep_approx.tau_tilde)[:,None]*K_x,lower=1)
+        v = mdot(self.Li,np.sqrt(self.ep_approx.tau_tilde)[:,None]*K_x)
+        variance = Kxx - np.dot(v.T,v)
+       	vdiag = np.diag(variance)
+        y=self.likelihood.predictive_mean(f,vdiag)
+        return f,vdiag,y
+
+    def plot(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None):
+        """
+        :param samples: the number of a posteriori samples to plot
+        :param which_data: which if the training data to plot (default all)
+        :type which_data: 'all' or a slice object to slice self.X, self.Y
+        :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
+        :param which_functions: which of the kernel functions to plot (additively)
+        :type which_functions: list of bools
+        :param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
+
+        Plot the posterior of the GP.
+          - In one dimension, the function is plotted with a shaded region identifying two standard deviations.
+          - In two dimsensions, a contour-plot shows the mean predicted function
+          - In higher dimensions, we've no implemented this yet !TODO!
+
+        Can plot only part of the data and part of the posterior functions using which_data and which_functions
+        """
+        if which_functions=='all':
+            which_functions = [True]*self.kern.Nparts
+        if which_data=='all':
+            which_data = slice(None)
+
+        X = self.X[which_data,:]
+        Y = self.Y[which_data,:]
+
+        Xorig = X*self._Xstd + self._Xmean
+        Yorig = Y*self._Ystd + self._Ymean
+        if plot_limits is None:
+            xmin,xmax = Xorig.min(0),Xorig.max(0)
+            xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
+        elif len(plot_limits)==2:
+            xmin, xmax = plot_limits
+        else:
+            raise ValueError, "Bad limits for plotting"
+
+        if self.X.shape[1]==1:
+            Xnew = np.linspace(xmin,xmax,resolution or 200)[:,None]
+            #m,v,phi = self.predict(Xnew,slices=which_functions)
+            #gpplot(Xnew,m,v)
+            mu_f, var_f, phi_f = self.predict(Xnew,slices=which_functions)
+            pb.subplot(211)
+            self.likelihood.plot1Da(X_new=Xnew,Mean_new=mu_f,Var_new=var_f,X_u=self.X,Mean_u=self.mu,Var_u=np.diag(self.Sigma))
+            if samples:
+                s = np.random.multivariate_normal(m.flatten(),v,samples)
+                pb.plot(Xnew.flatten(),s.T, alpha = 0.4, c='#3465a4', linewidth = 0.8)
+            pb.xlim(xmin,xmax)
+            pb.subplot(212)
+            self.likelihood.plot1Db(self.X,Xnew,phi_f)
+
+        elif self.X.shape[1]==2:
+            resolution = 50 or resolution
+            xx,yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
+            Xtest = np.vstack((xx.flatten(),yy.flatten())).T
+            zz,vv = self.predict(Xtest,slices=which_functions)
+            zz = zz.reshape(resolution,resolution)
+            pb.contour(xx,yy,zz,vmin=zz.min(),vmax=zz.max(),cmap=pb.cm.jet)
+            pb.scatter(Xorig[:,0],Xorig[:,1],40,Yorig,linewidth=0,cmap=pb.cm.jet,vmin=zz.min(),vmax=zz.max())
+            pb.xlim(xmin[0],xmax[0])
+            pb.ylim(xmin[1],xmax[1])
+
+        else:
+            raise NotImplementedError, "Cannot plot GPs with more than two input dimensions"
diff --git a/GPy/models/__init__.py b/GPy/models/__init__.py
index ab7ff5b4..5f824f2b 100644
--- a/GPy/models/__init__.py
+++ b/GPy/models/__init__.py
@@ -7,6 +7,7 @@ from sparse_GP_regression import sparse_GP_regression
 from GPLVM import GPLVM
 from warped_GP import warpedGP
 from GP_EP import GP_EP
+from GP_EP2 import GP_EP2
 from generalized_FITC import generalized_FITC
 from sparse_GPLVM import sparse_GPLVM
 from uncollapsed_sparse_GP import uncollapsed_sparse_GP